In biomedical image analysis, a fundamental problem is the segmentation of vessel in two-dimensional (2D)/three-dimensional (3D) images, to identify Target 2D/3D objects such as coronary vessel tree in CT and MRI images, and blood vessel segmentation in retinal images. Usually, in clinical practice, a vessel is manually segmented by expert operators, which is labor intensive and time-consuming, and the segmentation results may be subjective. Therefore, automatic computer-assisted segmentation is being developed to offer more reliable and consistent segmentation of vessels.
For example, combined patch-based CNNs with supervised decision fusion is applied to 2D image patches in an image for the analysis of the whole image. However, such conventional methods treat the 2D image patches as inputs independently. These methods ignore that image patches and their neighbors usually follow spatial patterns that are vital for the inference. For example, when a pixel is in the vessel region, its neighboring pixel also has a high probability to be labeled as vessel, since they are close to each other spatially. In addition, conventional segmentation methods process neighboring patches sequentially. That results in difficulties processing vessel bifurcation regions, which are very common in vessel tree analysis, such as coronary vessel tree segmentation.
Embodiments of the disclosure address the above problems by methods and systems for vessel refine segmentation using tree structure based deep learning model.